Fragmented data and disconnected systems in traditional marketing clouds prevent AI from forming a complete, persistent memory of customer interactions. This leads to missed opportunities and flawed personalization, as the AI operates with incomplete information, exposing foundational cracks in legacy architecture.

Walmart's primary view of AI is offensive, focusing on growth opportunities like creating a personalized, multimedia e-commerce experience. This shifts the narrative from AI as merely a defensive efficiency tool to a strategic growth driver, fundamentally changing how people shop.

Marketing leaders pressured to adopt AI are discovering the primary obstacle isn't the technology, but their own internal data infrastructure. Siloed, inconsistently structured data across teams prevents them from effectively leveraging AI for consumer insights and business growth.

While AI fragments shopping channels, it also enables hyper-personalization of the fulfillment experience. By integrating external data like weather, transit times, and regional issues, brands can proactively communicate with customers about their orders, creating a deeper, more valuable connection.

Stop thinking of sales, marketing, and support as separate functions with separate tools. AI agents are blurring these lines. A support interaction becomes a lead gen opportunity, and a marketing email can be sent by a 'sales' tool. Prepare for a unified go-to-market operational model.

Advanced retailers are moving beyond treating retail media as an ad channel for short-term sales. They integrate it with loyalty programs to deliver personalized value, which strengthens long-term customer relationships and retention, making it a strategic lever for growth.

AI will fragment the customer journey across countless platforms, moving purchases away from brand-owned websites. Retailers must build systems to manage inventory and product information across this decentralized landscape, not just focus on perfecting their own site experience.

Brands miss opportunities by testing product, packaging, and advertising in silos. Connecting these data sources creates a powerful feedback loop. For example, a consumer insight about desirable packaging can be directly incorporated into an ad campaign, but only if the data is unified.

To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.